scholarly journals Sea surface temperature changes due to polar lows over the Nordic Seas

Author(s):  
Pavel A. Golubkin ◽  
◽  
Julia E. Smirnova ◽  
Vsevolod S. Kolyada ◽  
◽  
...  

In this study possible changes in sea surface temperature (SST) caused by passage of polar lows and analyzed. Polar lows are extreme atmospheric phenomena inherent to high latitudes. They develop sea surface wind speeds from 15 m/s up to hurricane force values and are characterized by small sizes (on average, 300 km) and lifetimes (less than two days), which complicates their detection and studies. It is assumed that as in case of tropical cyclones, which may considerably lower SST due to intense mixing and entrainment of colder waters to the ocean upper mixed layer, polar lows could similarly influence SST. Moreover, in the high latitude areas, where salt stratification may be present instead of temperature stratification, SST may increase due to mixing with deeper warmer layer. In this study 330 polar lows were analyzed using satellite passive microwave radiometer measurements of SST. In result, 47 cases when average SST values changed in polar low forcing areas were found. Out of these cases, in six cases SST increase of at least 0.5 °С was found, and in fifteen cases SST decrease of at least 0.5 °С was found. This indicates that upper ocean response to polar lows is quite rare phenomenon, which should be further analyzed along with its possible role in the ocean-ice-atmosphere system.

2007 ◽  
Vol 24 (6) ◽  
pp. 1131-1142 ◽  
Author(s):  
Anant Parekh ◽  
Rashmi Sharma ◽  
Abhijit Sarkar

A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.


2011 ◽  
Vol 29 (2) ◽  
pp. 393-399
Author(s):  
T. I. Tarkhova ◽  
M. S. Permyakov ◽  
E. Yu. Potalova ◽  
V. I. Semykin

Abstract. Sea surface wind perturbations over sea surface temperature (SST) cold anomalies over the Kashevarov Bank (KB) of the Okhotsk Sea are analyzed using satellite (AMSR-E and QuikSCAT) data during the summer-autumn period of 2006–2009. It is shown, that frequency of cases of wind speed decreasing over a cold spot in August–September reaches up to 67%. In the cold spot center SST cold anomalies reached 10.5 °C and wind speed lowered down to ~7 m s−1 relative its value on the periphery. The wind difference between a periphery and a centre of the cold spot is proportional to SST difference with the correlations 0.5 for daily satellite passes data, 0.66 for 3-day mean data and 0.9 for monthly ones. For all types of data the coefficient of proportionality consists of ~0.3 m s−1 on 1 °C.


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